Added the fast-burnin option for EW

gstlal-inspiral/bin/gstlal_inspiral_marginalize_likelihoods_online

@@ -82,6 +82,7 @@ def parse_command_line():
 	parser.add_option("--output-kafka-server", metavar = "addr", help = "Set the server address and port number for output data. Optional, e.g., 10.14.0.112:9092")
 	parser.add_option("--tag", metavar = "string", default = "test", help = "Sets the name of the tag used. Default = 'test'")
 	parser.add_option("--ifo", metavar = "ifo", action = "append", help = "ifos with which to create output filenames if they don't already exist")
+	parser.add_option("--fast-burnin", action = "store_true", default = False, help = "Set whether to enable fast burn-in or not. If enabled, only 67% of bins are required to contribute to the marginalized PDF")
 	parser.add_option("--verbose", action = "store_true", help = "Be verbose.")
 	options, filenames = parser.parse_args()
 
@@ -365,7 +366,12 @@ def main():
 		# write output document only if 99% of bins have been extincted and
 		# hence have contributed to the noise diststat PDF. Otherwise, the 
 		# PDF will not be representative of the LRs across all bins
-		if num_extincted >= 0.99 * len(registries):
+		# If fast burn-in is enabled, this value is changed to 67%. This is
+		# taken from how many bins are ready_for_extinction after a 2 week
+		# period in an HL EW analysis with 1000 templates / bin
+		# NOTE: This option is not meant to be used for analyses other than
+		# the one mentioned above.
+		if num_extincted >= 0.99 * len(registries) or (options.fast_burnin and num_extincted >= 0.666667 * len(registries)):
 			xmldoc = ligolw.Document()
 			xmldoc.appendChild(ligolw.LIGO_LW())
 			process = ligolw_process.register_to_xmldoc(xmldoc, sys.argv[0], paramdict = {})

gstlal-inspiral/python/dags/layers/inspiral.py

@@ -1923,6 +1923,10 @@ def marginalize_online_layer(config, dag, marg_pdf_cache):
 		Option("tag", config.tag),
 		Option("verbose"),
 	]
+
+	if config.rank.fast_burnin:
+		arguments.append(Option("fast-burnin"))
+
 	layer += Node(arguments = arguments)
 
 	dag.attach(layer)